Multimodal mAgnetic Resonance imaGIng in Cardiovascular Disease

Official title

Multimodal-MRI in Cardiovascular Diseases

Overview

This single-center, prospective, observational cohort study aims to evaluate the clinical application value of multi-modal cardiovascular magnetic resonance (CMR) imaging in patients with cardiovascular diseases (CVD). While traditional imaging methods have limitations in fully evaluating myocardial tissue characteristics, multi-modal CMR offers a comprehensive, non-invasive "one-stop" assessment. It can simultaneously evaluate heart structure, function, tissue features (such as fibrosis and edema), and hemodynamics. The study plans to enroll patients with suspected or confirmed CVD. Participants will undergo a comprehensive multi-modal CMR scan (including Cine, T1/T2 mapping, Late Gadolinium Enhancement, and 4D flow sequences) as part of their evaluation. In addition to clinical evaluation, the study will explore sequence optimization (e.g., comparing pre-contrast vs. post-contrast Cine, and 3-slice vs. 9-slice T1 mapping) and evaluate deep learning-based virtual native enhancement (VNE) models trained under different sequence protocols. By tracking clinical outcomes, the study seeks to establish a standardized imaging assessment system to improve the early detection, accurate diagnosis, risk stratification, and prognostic prediction for various types of cardiovascular diseases.

Detailed description

Cardiovascular disease (CVD) remains a leading cause of global mortality and disability. Accurate and early assessment of cardiac structure, function, and myocardial tissue characteristics is crucial for optimal clinical management. Cardiac Magnetic Resonance (CMR) has evolved from single morphological imaging into advanced multi-modal imaging. By integrating Cine, Late Gadolinium Enhancement (LGE), T1/T2 mapping, and 4D flow techniques, multi-modal CMR serves as a "gold standard" that provides a comprehensive macroscopic and cellular-level evaluation, including the identification of myocardial fibrosis, edema, and complex hemodynamic alterations.

Despite its clinical potential, systematic research comparing the diagnostic efficacy and prognostic value of multi-modal CMR features across a broad spectrum of cardiovascular diseases (such as ischemic heart disease, non-ischemic cardiomyopathy, and valvular diseases) is still lacking. Furthermore, optimization of scan efficiency, standardization of sequence acquisition protocols, and the development of contrast-free or AI-driven virtual imaging techniques (such as deep learning-based virtual native enhancement ) represent critical avenues to enhance clinical utility.

This prospective, observational registry study is designed to address this gap by establishing a large-scale, standardized CMR imaging database. Approximately 2,000 patients with clinically suspected or confirmed CVD will be consecutively enrolled. Following routine clinical care pathways, participants will undergo a "one-stop" multi-modal CMR examination using 3.0T MRI scanners.

To refine imaging protocols and validate novel synthetic imaging algorithms, a subset/sub-cohort analysis will specifically investigate the impacts of acquisition parameters-comparing performance between pre-contrast Cine vs. post-contrast Cine, as well as 3-slice vs. 9-slice T1 mapping protocols-on downstream machine learning/deep learning models for virtual native enhancement and tissue characterization.

The primary objectives are to: 1) systematically delineate the imaging feature spectrum across different CVD subtypes; 2) optimize multi-modal CMR acquisition protocols and validate deep learning models for virtual image generation (e.g., VNE); 3) assess the sensitivity of hemodynamic and tissue-characterization parameters (especially T1 mapping and extracellular volume \[ECV\]) in detecting early cardiac damage; and 4) explore the correlation between multi-modal CMR parameters and major adverse cardiovascular events (MACE) during the follow-up period. Ultimately, this study aims to provide robust, evidence-based support for precision diagnosis and risk stratification in cardiovascular medicine.

Interventions

• Diagnostic test Multi-modal Cardiac Magnetic Resonance
  A comprehensive "one-stop" scanning protocol using 3.0T MRI scanners. The protocol includes Cine imaging, T1/T2 mapping, Late Gadolinium Enhancement (LGE), first-pass perfusion, and 4D flow sequences to evaluate cardiac structure, function, myocardial tissue characteristics, and hemodynamics.

Primary outcome measures

• Diagnostic Efficacy of Multi-modal CMR Parameters (AUC) [Time frame: Baseline (at the time of CMR scan)]

Secondary outcome measures (5)

• Correlation Between Imaging Parameters and Clinical Indicators [Time frame: Baseline]
• Incidence of Major Adverse Cardiovascular Events (MACE) [Time frame: Up to 3 years]
• Model performance for virtual native enhancement across different sequence acquisition protocols [Time frame: At baseline]
• Model performance for virtual LGE generation across sequence protocols: Peak Signal-to-Noise Ratio (PSNR) [Time frame: At baseline]
• Model performance for virtual LGE generation across sequence protocols: DICE coefficient [Time frame: At baseline]

Eligibility criteria

Inclusion criteria

• Aged 18 years and older, with no gender restrictions.

Clinically suspected or confirmed cardiovascular disease (including but not limited to ischemic heart disease, non-ischemic cardiomyopathy, myocarditis, valvular disease, etc.), requiring a cardiac magnetic resonance (CMR) examination to determine the etiology or evaluate myocardial tissue characteristics.

No contraindications to magnetic resonance examination, and able to cooperate with breath-holding instructions.

Voluntarily participate in this study and sign a written informed consent form.

Exclusion criteria

• Absolute contraindications: Implantation of non-MRI compatible metallic foreign bodies (e.g., old pacemakers, implantable cardioverter-defibrillators \[ICD\], aneurysm clips, etc.).

Relative contraindications: Severe claustrophobia, unable to complete the examination despite communication.

Severe renal insufficiency.

Special populations: Pregnant or lactating women.

Presence of severe arrhythmias (e.g., persistent atrial fibrillation) leading to severely impaired magnetic resonance signal acquisition, rendering the image quality inadequate for diagnosis.

Poor expected compliance: Unable to complete follow-up, or deemed unsuitable for enrollment by the investigator for other reasons.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

China · 1 center

• The Second Affiliated Hospital Zhejiang University School of Medicine — Hangzhou

Publications

• Paiva L, Ferreira MJ, Afonso S, Donato P, Goncalves L. Cardiac T1 mapping in non-ST-segment elevation myocardial infarction: temporal changes in myocardial fibrosis. Front Cardiovasc Med. 2025 May 23;12:1563368. doi: 10.3389/fcvm.2025.1563368. eCollection 2025. PMID 40486822
• Eichhorn C, Greulich S, Bucciarelli-Ducci C, Sznitman R, Kwong RY, Grani C. Multiparametric Cardiovascular Magnetic Resonance Approach in Diagnosing, Monitoring, and Prognostication of Myocarditis. JACC Cardiovasc Imaging. 2022 Jul;15(7):1325-1338. doi: 10.1016/j.jcmg.2021.11.017. Epub 2022 Jan 12. PMID 35592889
• Leong DP, Joseph PG, McKee M, Anand SS, Teo KK, Schwalm JD, Yusuf S. Reducing the Global Burden of Cardiovascular Disease, Part 2: Prevention and Treatment of Cardiovascular Disease. Circ Res. 2017 Sep 1;121(6):695-710. doi: 10.1161/CIRCRESAHA.117.311849. PMID 28860319

Identifiers

NCT: NCT07617844 · 2026-0319

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗